C-ECAFormer: A new lightweight fault diagnosis framework towards heavy noise and small samples

In engineering practice, small-sample fault diagnosis of mechanical equipment towards heavy noise interference poses great challenges for the existing Transformer based intelligent models. To address these challenges, this paper proposes a new lightweight model called C-ECAFormer. Firstly, inverted residual block is used to establish signal correlations and inductive bias capability and to extract richer local feature information by varying the input channel dimensions. Secondly, ECAFormer module is designed to enhance the relationship awareness between different channels in the input signal features, thereby improving the model's attention to important channels. Finally, collaborative self-attention block is developed to facilitate spatial interaction between window local and grid global in vibration signals, reducing the number of parameters and computational complexity of the model. The results of two experiments demonstrate that the proposed approach accommodates advantages of lightweight and robustness in small-sample fault diagnosis tasks, compared to the existing mainstream Transformer and CNN fault diagnosis frameworks.